Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases3 cases

1 to 3 of 3

  1. Symptoms

    At 13:42 UTC on 19 May 2025, RebelMouse's monitoring tools reported a higher error rate and degraded performance across services. A routine reroll of the EKS nodes had started 27 minutes earlier, at 13:15 UTC.

    Trigger

    At 13:15 UTC RebelMouse started its regular reroll of EKS nodes to apply security patches. The planned, automatic termination of an EC2 instance terminated one of the CoreDNS pods.

    Root cause
    Component
    Contributing factor
    Detection

    Monitoring tools detected an increased error rate and degraded service performance at 13:42 UTC, and the tech team started investigating at 13:44 UTC.

    Remediation
    • rescheduled the affected CoreDNS pod onto a new EC2 instance
    • scaled up the CoreDNS deployment to more replicas so DNS load was spread evenly across all pods
    Action items
    • Add scale-in protection so CoreDNS pods are not terminated unexpectedly during peak load
    • Review the CoreDNS configuration so that vertical scaling of the pods works
    • Hold regular capacity reviews so the CoreDNS deployment is provisioned for peak load
  2. Symptoms

    In January 2022 an etcd memory spike overloaded the Kubernetes control plane of one of Render's clusters in Frankfurt. At first only operations that need the control plane failed: new deploys and cron job runs.

    Trigger

    A memory spike in etcd overloaded the cluster's control plane.

    Root cause
    Component
    Contributing factor
    Action items
    • Run CoreDNS on data plane nodes instead of control plane nodes
    • Run etcd on dedicated nodes
    Lessons

    CoreDNS can keep answering from its in-memory cache while the API server is down, but it cannot initialise without it. With CoreDNS on the control plane nodes, a reboot or restart during a control plane outage turned a failure of deploys and cron jobs into a failure of requests to services that were already running. The core problem was that the data plane was not isolated enough from the control plane: even without the reboot, the etcd memory spike alone could have forced CoreDNS to restart.

  3. Symptoms

    Customers of the Zalando Fashion Store got a high rate of errors on all web product pages and outfit pages for more than an hour. The errors came from the main data aggregation layer service, which runs in one of Zalando's Kubernetes clusters.

    Trigger

    Calls from the aggregation layer to a single, not very important downstream service started timing out. The aggregation layer returned 404s for them, clients retried, and the resulting spike in requests became a spike in DNS queries to CoreDNS.

    Root cause
    Component
    Contributing factor
    Detection

    The on-call engineer for the aggregation layer phoned the on-call Kubernetes engineer. Automatic paging did not happen because the cluster's monitoring and alerting ran inside the cluster and failed along with DNS. The Kubernetes engineer then saw that the CoreDNS pods were being OOM-killed.

    Remediation
    • Raised the CoreDNS memory requests and limits by hand from 100Mi to 2000Mi
    Action items
    • Introduce a more resilient DNS setup: CoreDNS fronted by dnsmasq on every node
    • Improve high-level alerting from outside the clusters, so that a cluster problem is known before or at the same time as applications are affected
    • Improve incident response processes and tooling so that any on-call engineer can escalate directly to other teams when automatic paging fails, for example through a chat bot
    Lessons

    The Kubernetes DNS setup was far from resilient enough: a single application could take down DNS for everything in the cluster. The blast radius of one application sending more DNS queries should be limited to one node at most, ideally to single pods. Known Kubernetes DNS problems, such as Linux kernel races in conntrack handling and the ndots 5 problem, were familiar to the team and partly mitigated, but their combined effect on the DNS infrastructure was worse than the team had understood. Monitoring that runs inside the cluster was not robust enough: when the cluster's DNS broke, it could not page anyone, and recovery took longer than it should have.

Check your cluster for this

These 3 cases involve CoreDNS and Client-side DNS resolver and cache. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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